Several Intrusion Detection Techniques (IDTs) projected for mobile unexpected networks suppose every node passively watching the info forwarding by its next hop. This paper presents quantitative evaluations of false positives and their impact on watching based mostly intrusion detection for unexpected networks. Experimental results show that, even for a straightforward three-node configuration, associate actual adhoc network suffers from high false positives; these results area unit valid by mathematician and probabilistic models. However, this false positive downside can't be determined by simulating constant network mistreatment common unexpected network simulators, like ns-2, OPNET or Glomosim. To remedy this, a probabilistic noise generator model is enforced within the Glomosim machine. With this revised noise model, the simulated network exhibits the combination false positive behavior just like that of the experimental testbed. Simulations of larger (50-node) unexpected networks indicate that monitoring-based intrusion detection has terribly high false positives. These false positives will scale back the network performance or increase the overhead. in a very easy monitoring-based system wherever no secondary and additional correct strategies area unit used, the false positives impact the network performance in 2 ways: reduced turnout in traditional networks while not attackers and inability to mitigate the impact of attacks in networks with attackers.